*! version 3.4.0  12jan2015
*  Rewrite of program contributed by:
*  Lawrence C. Hamilton, 1990, University of New Hampshire
*  Author of REGRESSION WITH GRAPHICS, Belmont, Calif.: Duxbury Press
program define rreg2, eclass byable(recall) sort prop(mi)
        version 6, missing
        local options "Level(cilevel)"
        if replay() {
                if "`e(cmd)'"!="rreg" { error 301 }
                if _by() { error 190 }
                syntax [, `options' *]
                _get_diopts diopts, `options'
        }
        else {
                local vv : display "version " string(_caller()) ", missing:"
                local cmdline : copy local 0
                syntax varlist(fv ts) [if] [in] [, `options' /*
                        */ noLOg Graph TOlerance(real .01) TUne(real 7) /*
                        */ Genwt(string) ITerate(integer 1000) *]
                if "`s(fvops)'" == "true" | _caller() >= 11 {
                        if _caller() < 11 {
                                local vv "version 11: "
                        }
                        local fvexp "expand"
                }
                _get_diopts diopts, `options'
                if _by() {
                        _byoptnotallowed genwt() `"`genwt'"'
                }
                if `toleran' <=0 {
                        di in red "tolerance() must be positive"
                        exit 198
                }
                if `iterate' <=0 {
                        di in red "iterate() must be positive"
                        exit 198
                }
                if `tune' <= 0 {
                        di in red "tune() must be positive"
                        exit 198
                }
                if "`genwt'" != "" {
                        confirm new variable `genwt'
                }

                tokenize `varlist'
                local lhs "`1'"
                _fv_check_depvar `lhs'
                tsunab lhs : `lhs'
                mac shift
                local rhs "`*'"
                *estimates clear

                tempvar res absdev maxd weight y oldw touse
                tempname max scale aa lambda

                local tune = `tune'*4.685/7
                local log = cond("`log'"=="", "noisily", "*")

                mark `touse' `if' `in'
                markout `touse' `lhs' `rhs'

                `vv' _rmcoll `rhs' if `touse', `fvexp'
quietly {

                `vv' ///
                _regress `lhs' `rhs' if `touse', noconst
        /* Omit obs with Cook's D > 1. */       
                _predict double `weight' if `touse', cook
                replace `touse' = 0 if `weight' > 1 & `touse'
                replace `weight' = 1 if `touse'
                `vv' ///
                _regress `lhs' `rhs' if `touse', noconst
                _predict double `res' if `touse', residual
                sum `res' if `touse', detail
                gen double `absdev' = abs(`res'-r(p50)) if `touse'
                gen double `maxd' = 1 if `touse'
                `log' di 

                local it 1
                scalar `max' = 1
                while `max' > 5*`toleran' & `it' <= `iterate' {
                        capture drop `oldw'
                        rename `weight' `oldw'
                        sum `absdev' if `touse', detail
                        gen double `weight' = cond(             /*
                        */      abs(`res')>2*r(p50),            /* 
                        */      2*r(p50)/abs(`res'), 1 )        /*
                        */      if `touse'
                        `vv' ///
                        _regress `lhs' `rhs' [aw=`weight'] if `touse', noconst
                        if "`graph'"!="" {
                                version 8: graph twoway         ///
                                (scatter `weight' `oldw' `oldw' ///
                                        if `touse',             ///
                                        sort                    ///
                                        msymbol(o i)            ///
                                        connect(i l)            ///
                                        ytitle("New weight")    ///
                                        xtitle("Old weight")    ///
                                        legend(nodraw)          ///
                                )
                        }
                        drop `res'
                        _predict double `res' if `touse', residual
                        sum `res' if `touse', detail
                        replace `absdev'= abs(`res'-r(p50)) if `touse'
                        replace `maxd'=abs(`weight'-`oldw') if `touse'
                        sum `maxd' if `touse', meanonly
                        scalar `max' = r(max)
                        `log' di in gr "   Huber iteration `it':  " /*
                                */ "maximum difference in weights = " /*
                                */ in ye `max'
                        local it = `it'+1
                }

                if `max' > 5*`toleran' {
                        noi di in blu "Warning: Huber iterations" /*
                        */ " did not converge in `iterate' iterations"
                }

                scalar `max' = 1
                local notyet 1
                while (`max'>`toleran' & `it'<=`iterate') | `notyet' {
                        local notyet 0
                        capture drop `oldw'
                        rename `weight' `oldw'
                        sum `absdev' if `touse', detail
                        scalar `scale' = r(p50)/.6745
                        gen double `weight'= /*
                        */ max(1-(`res'/(`tune'*`scale'))^2,0)^2 /* 
                        */ if `touse'
                        count if `weight' != 0 & `touse'
                        if r(N) == 0 {
                                di as err "all weights went to zero;"
                                error 2000
                        }
                        `vv' ///
                        _regress `lhs' `rhs' [aw=`weight'] if `touse', noconst
                        if "`graph'"!="" {
                                version 8: graph twoway         ///
                                (scatter `weight' `oldw' `oldw' ///
                                        if `touse',             ///
                                        sort                    ///
                                        msymbol(o i)            ///
                                        connect(i l)            ///
                                        ytitle("New weight")    ///
                                        xtitle("Old weight")    ///
                                        legend(nodraw)          ///
                                )
                        }
                        drop `res'
                        _predict double `res' if `touse', residual
                        sum `res' if `touse', detail
                        replace `absdev' = abs(`res'-r(p50)) /*
                                */ if `touse'
                        replace `maxd'=abs(`weight'-`oldw') if `touse'
                        sum `maxd' if `touse', meanonly
                        scalar `max' = r(max)
                        `log' di in gr "Biweight iteration `it':  " /*
                        */ "maximum difference in weights = " /*
                        */ in ye `max'
                        local it = `it'+1
                }

                if `max' > `toleran' {
                        noi di in blu _n "Warning: Did not converge" /*
                        */ " in `iterate' iterations"
                }

                replace `absdev' = (1-(1/`tune'^2)* /*
                        */ (`res'/`scale')^2)*(1-(5/`tune'^2)* /* 
                        */ (`res'/`scale')^2) if `touse'
                replace `absdev' = 0 /*
                        */ if abs(`res'/`scale')>`tune' & `touse'
                sum `absdev' if `touse', meanonly
                scalar `aa' = r(mean)
                scalar `lambda' = 1+((e(df_m)+1)/e(N))* /*
                        */ (1-`aa')/`aa'
                drop `absdev' `maxd' `oldw'
                _predict double `y' if `touse'
                replace `y'= `y' + /* 
                */ (`lambda'*`scale'/`aa')*(`res'/`scale')*`weight' /*
                */ if `touse'
                `vv' ///
                _regress `y' `rhs' if `touse', dep(`lhs') noconst
                if "`genwt'"!="" {
                        rename `weight' `genwt'
                        label var `genwt' "Robust Regression Weight"
                }
                est local ll
                est local ll_0
                est local genwt `genwt'
                est local estat_cmd ""          // reset to empty
                est repost, buildfvinfo
                est local predict "rreg_p"
                est local title "Robust regression"
                version 10: ereturn local cmdline `"rreg `cmdline'"'
                est local cmd "rreg"
                _post_vce_rank
                global S_E_cmd "`e(cmd)'"       /* double save */

} // quietly

        }

        if "`e(prefix)'" == "" {
                #delimit ;
                di _n
                        in gr "Robust regression"
                        in gr _col(49) "Number of obs"
                              _col(67) "= " in ye %10.0fc e(N) _n
                        in gr _col(49) "F(" %3.0f e(df_m) "," %10.0f e(df_r)    
                                        ")"
                              _col(67) "= " in ye %10.2f e(F) _n
                        in gr _col(49) "Prob > F"
                              _col(67) "= " in ye %10.4f
                                        fprob(e(df_m), e(df_r), e(F)) _n ; 
                #delimit cr
                local head noheader
        }
        regress, `head' level(`level') `diopts'
end
exit
